IDEAS home Printed from https://ideas.repec.org/p/wbk/wbrwps/6372.html
   My bibliography  Save this paper

What does variation in survey design reveal about the nature of measurement errors in household consumption ?

Author

Listed:
  • Gibson, John
  • Beegle, Kathleen
  • De Weerdt, Joachim
  • Friedman, Jed

Abstract

This paper uses data from eight different consumption questionnaires randomly assigned to 4,000 households in Tanzania to obtain evidence on the nature of measurement errors in estimates of household consumption. While there are no validation data, the design of one questionnaire and the resources put into its implementation make it likely to be substantially more accurate than the others. Comparing regressions using data from this benchmark design with results from the other questionnaires shows that errors have a negative correlation with the true value of consumption, creating a non-classical measurement error problem for which conventional statistical corrections may be ineffective.

Suggested Citation

  • Gibson, John & Beegle, Kathleen & De Weerdt, Joachim & Friedman, Jed, 2013. "What does variation in survey design reveal about the nature of measurement errors in household consumption ?," Policy Research Working Paper Series 6372, The World Bank.
    • Handle: RePEc:wbk:wbrwps:6372
    as

    Download full text from publisher

    File URL: http://www-wds.worldbank.org/external/default/WDSContentServer/WDSP/IB/2013/02/21/000158349_20130221134732/Rendered/PDF/wps6372.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. John Gibson & Bonggeun Kim, 2010. "Non‐Classical Measurement Error in Long‐Term Retrospective Recall Surveys," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(5), pages 687-695, October.
    2. Andrew Chesher & Christian Schluter, 2002. "Welfare Measurement and Measurement Error," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 69(2), pages 357-378.
    3. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    4. Pischke, Jorn-Steffen, 1995. "Measurement Error and Earnings Dynamics: Some Estimates from the PSID Validation Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 305-314, July.
    5. Shahidur R. Khandker, 2005. "Microfinance and Poverty: Evidence Using Panel Data from Bangladesh," The World Bank Economic Review, World Bank, vol. 19(2), pages 263-286.
    6. Alderman, Harold & Hoogeveen, Hans & Rossi, Mariacristina, 2006. "Reducing child malnutrition in Tanzania: Combined effects of income growth and program interventions," Economics & Human Biology, Elsevier, vol. 4(1), pages 1-23, January.
    7. Angus Deaton & Christina Paxson, 1998. "Economies of Scale, Household Size, and the Demand for Food," Journal of Political Economy, University of Chicago Press, vol. 106(5), pages 897-930, October.
    8. Beegle, Kathleen & De Weerdt, Joachim & Friedman, Jed & Gibson, John, 2012. "Methods of household consumption measurement through surveys: Experimental results from Tanzania," Journal of Development Economics, Elsevier, vol. 98(1), pages 3-18.
    9. Menno Pradhan, 2001. "Welfare Analysis with a Proxy Consumption Measure – Evidence from a Repeated Experiment in Indonesia," Tinbergen Institute Discussion Papers 01-092/2, Tinbergen Institute.
    10. John Gibson, 2002. "Why Does the Engel Method Work? Food Demand, Economies of Size and Household Survey Methods," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(4), pages 341-359, September.
    11. Gibson, John, 2002. "Why Does the Engel Method Work? Food Demand, Economies of Size and Household Survey Methods," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(4), pages 341-359, September.
    12. John Gibson & Bonggeun Kim, 2007. "Measurement Error in Recall Surveys and the Relationship between Household Size and Food Demand," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(2), pages 473-489.
    13. Naeem Ahmed & Matthew Brzozowski & Thomas Crossley, 2006. "Measurement errors in recall food consumption data," IFS Working Papers W06/21, Institute for Fiscal Studies.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jayasinghe, Maneka & Chai, Andreas & Ratnasiri, Shyama & Smith, Christine, 2017. "The power of the vegetable patch: How home-grown food helps large rural households achieve economies of scale & escape poverty," Food Policy, Elsevier, vol. 73(C), pages 62-74.
    2. Fiedler, John L. & Mwangi, Dena M., 2016. "Improving household consumption and expenditure surveys’ food consumption metrics: Developing a strategic approach to the unfinished agenda:," IFPRI discussion papers 1570, International Food Policy Research Institute (IFPRI).
    3. Brzozowski, Matthew & Crossley, Thomas F. & Winter, Joachim K., 2017. "Does survey recall error explain the Deaton–Paxson puzzle?," Economics Letters, Elsevier, vol. 158(C), pages 18-20.
    4. Trevon D. Logan, 2011. "Economies Of Scale In The Household: Puzzles And Patterns From The American Past," Economic Inquiry, Western Economic Association International, vol. 49(4), pages 1008-1028, October.
    5. Perali, Federico, 2008. "The second Engel law: Is it a paradox?," European Economic Review, Elsevier, vol. 52(8), pages 1353-1377, November.
    6. Carletto,Calogero & Deininger,Klaus W. & Muwonge, James & Savastano,Sara & Carletto,Calogero & Deininger,Klaus W. & Muwonge, James & Savastano,Sara, 2011. "Can diaries help improve agricultural production statistics ? Evidence from Uganda," Policy Research Working Paper Series 5717, The World Bank.
    7. Timothy J. Halliday, 2010. "Mismeasured Household Size and its Implications for the Identification of Economies of Scale," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(2), pages 246-262, April.
    8. Calogero Carletto & Dean Jolliffe & Raka Banerjee, 2015. "From Tragedy to Renaissance: Improving Agricultural Data for Better Policies," Journal of Development Studies, Taylor & Francis Journals, vol. 51(2), pages 133-148, February.
    9. Nicole Jonker & Anneke Kosse, 2013. "Estimating Cash Usage: The Impact of Survey Design on Research Outcomes," De Economist, Springer, vol. 161(1), pages 19-44, March.
    10. Brzozowski, Matthew & Crossley, Thomas F. & Winter, Joachim K., 2017. "A comparison of recall and diary food expenditure data," Food Policy, Elsevier, vol. 72(C), pages 53-61.
    11. Deininger, Klaus & Carletto, Calogero & Savastano, Sara & Muwonge, James, 2012. "Can diaries help in improving agricultural production statistics? Evidence from Uganda," Journal of Development Economics, Elsevier, vol. 98(1), pages 42-50.
    12. Conforti, Piero & Grünberger, Klaus & Troubat, Nathalie, 2017. "The impact of survey characteristics on the measurement of food consumption," Food Policy, Elsevier, vol. 72(C), pages 43-52.
    13. Gibson, John, 2003. "Does Measurement Error Explain a Paradox About Household Size and Food Demand? Evidence from Variation in Household Survey Methods," 2003 Annual meeting, July 27-30, Montreal, Canada 22198, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    14. Blessing M. Chiripanhura & Miguel Niño-Zarazúa, 2016. "The impacts of the food, fuel and financial crises on poor and vulnerable households in Nigeria: A retrospective approach to research inquiry," Development Policy Review, Overseas Development Institute, vol. 34(6), pages 763-788, November.
    15. Abay, Kibrom A. & Berhane, Guush & Hoddinott, John F. & Tafere, Kibrom, 2021. "Assessing response fatigue in phone surveys: Experimental evidence on dietary diversity in Ethiopia," IFPRI discussion papers 2017, International Food Policy Research Institute (IFPRI).
    16. Sanae Tashiro, 2009. "Differences in Food Preparation by Race and Ethnicity: Evidence from the American Time Use Survey," The Review of Black Political Economy, Springer;National Economic Association, vol. 36(3), pages 161-180, December.
    17. de Nicola, Francesca & Giné, Xavier, 2014. "How accurate are recall data? Evidence from coastal India," Journal of Development Economics, Elsevier, vol. 106(C), pages 52-65.
    18. Karbasi, A. & Mohammadzadeh, S.H., 2018. "Estimating Household Expenditure Economies of Scale in Iran," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277152, International Association of Agricultural Economists.
    19. Echeverría, Lucía & Molina, José Alberto, 2022. "Exploring household heterogeneities of the Deaton-Paxson puzzle: Evidence for Argentina," Nülan. Deposited Documents 3622, Universidad Nacional de Mar del Plata, Facultad de Ciencias Económicas y Sociales, Centro de Documentación.
    20. Niño-Zarazúa, Miguel & Chiripanhura, Blessing, 2013. "The impacts of the food, fuel and financial crises on households in Nigeria. A retrospective approach for research enquiry," MPRA Paper 47348, University Library of Munich, Germany.

    More about this item

    Keywords

    Food&Beverage Industry; Consumption; Inequality; Economic Theory&Research; Statistical&Mathematical Sciences;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wbk:wbrwps:6372. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Roula I. Yazigi (email available below). General contact details of provider: https://edirc.repec.org/data/dvewbus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.